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stat.ML updates on arXiv.org

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A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
Tim Y. J. Wang, O. Deniz Akyildiz · 2025-09-24 · via stat.ML updates on arXiv.org

Solving ill-posed inverse problems requires powerful and flexible priors. We propose leveraging pretrained latent diffusion models for this task through a new training-free approach, termed Diffusion-regularized Wasserstein Gradient Flow (DWGF). Specifically, we formulate the posterior sampling problem as a regularized Wasserstein gradient flow of the Kullback-Leibler divergence in the latent space. We demonstrate the performance of our method on standard benchmarks using StableDiffusion (Rombach et al., 2022) as the prior.

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